Article

From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research

2023-01-02

Abstract excerpt

<p>Advances in computational statistics and corresponding shifts in funding initiatives over the past few decades have led to a proliferation of neuroscientific measures being developed in the context of mental health research. Although such measures have undoubtedly deepened our understanding of neural mechanisms underlying cognitive, affective, and behavioral processes associated with various mental health condi...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 62123cf5-a0b9-585b-9f78-f9c9b2a3d7b8
DOI: 10.31234/osf.io/ax34v

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health ResearchDOI 10.31234/osf.io/ax34vRead

Theory-Driven Computational Psychiatry and Its Challenges: An Optimistic OutlookSemantic proximity Read

Brain Diffusion Transformer for Personalized Neuroscience and PsychiatrySemantic proximity Read

Cognitive arbitration between candidate dimensions of psychopathologySemantic proximity Read

Behavioral biomarker reliability can mislead mechanistic inference in precision psychiatrySemantic proximity Read

Individual Differences in Computational Psychiatry: A Review of Current ChallengesSemantic proximity Read

Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and PromisesSemantic proximity Read

Predictive Coding and Neurocomputational Psychiatry: A Mechanistic Framework for Understanding Mental DisordersSemantic proximity Read

Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain ImagingSemantic proximity Read

Bayesian Workflow for Generative Modeling in Computational PsychiatrySemantic proximity Read